#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# pytype: skip-file
# mypy: check-untyped-defs

import atexit
import copy
import functools
import itertools
import logging
import threading
import time
from typing import Any
from typing import Dict
from typing import Iterator
from typing import Optional
from typing import Tuple

import grpc
from google.protobuf import struct_pb2

from apache_beam.metrics import metric
from apache_beam.metrics.execution import MetricResult
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import PortableOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.value_provider import ValueProvider
from apache_beam.pipeline import Pipeline
from apache_beam.portability import common_urns
from apache_beam.portability import python_urns
from apache_beam.portability.api import beam_artifact_api_pb2_grpc
from apache_beam.portability.api import beam_job_api_pb2
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners import runner
from apache_beam.runners.job import utils as job_utils
from apache_beam.runners.portability import artifact_service
from apache_beam.runners.portability import job_server
from apache_beam.runners.portability import portable_metrics
from apache_beam.runners.portability.fn_api_runner.fn_runner import translations
from apache_beam.runners.worker import sdk_worker_main
from apache_beam.runners.worker import worker_pool_main
from apache_beam.transforms import environments

__all__ = ['PortableRunner']

MESSAGE_LOG_LEVELS = {
    beam_job_api_pb2.JobMessage.MESSAGE_IMPORTANCE_UNSPECIFIED: logging.INFO,
    beam_job_api_pb2.JobMessage.JOB_MESSAGE_DEBUG: logging.DEBUG,
    beam_job_api_pb2.JobMessage.JOB_MESSAGE_DETAILED: logging.DEBUG,
    beam_job_api_pb2.JobMessage.JOB_MESSAGE_BASIC: logging.INFO,
    beam_job_api_pb2.JobMessage.JOB_MESSAGE_WARNING: logging.WARNING,
    beam_job_api_pb2.JobMessage.JOB_MESSAGE_ERROR: logging.ERROR,
}

TERMINAL_STATES = [
    beam_job_api_pb2.JobState.DONE,
    beam_job_api_pb2.JobState.DRAINED,
    beam_job_api_pb2.JobState.FAILED,
    beam_job_api_pb2.JobState.CANCELLED,
]

_LOGGER = logging.getLogger(__name__)


class JobServiceHandle(object):
  """
  Encapsulates the interactions necessary to submit a pipeline to a job service.

  The base set of interactions consists of 3 steps:
  - prepare
  - stage
  - run
  """
  def __init__(self, job_service, options, retain_unknown_options=False):
    self.job_service = job_service
    self.options = options
    self.timeout = options.view_as(PortableOptions).job_server_timeout
    self.artifact_endpoint = options.view_as(PortableOptions).artifact_endpoint
    self._retain_unknown_options = retain_unknown_options

  def submit(
      self, proto_pipeline: beam_runner_api_pb2.Pipeline
  ) -> Tuple[str,
             Iterator[beam_job_api_pb2.JobStateEvent],
             Iterator[beam_job_api_pb2.JobMessagesResponse]]:
    """
    Submit and run the pipeline defined by `proto_pipeline`.
    """
    prepare_response = self.prepare(proto_pipeline)
    artifact_endpoint = (
        self.artifact_endpoint or
        prepare_response.artifact_staging_endpoint.url)
    self.stage(
        proto_pipeline,
        artifact_endpoint,
        prepare_response.staging_session_token)
    return self.run(prepare_response.preparation_id)

  def get_pipeline_options(self) -> struct_pb2.Struct:
    """
    Get `self.options` as a protobuf Struct
    """

    # fetch runner options from job service
    # retries in case the channel is not ready
    def send_options_request(max_retries=5):
      num_retries = 0
      while True:
        try:
          # This reports channel is READY but connections may fail
          # Seems to be only an issue on Mac with port forwardings
          return self.job_service.DescribePipelineOptions(
              beam_job_api_pb2.DescribePipelineOptionsRequest(),
              timeout=self.timeout)
        except grpc.FutureTimeoutError:
          # no retry for timeout errors
          raise
        except grpc.RpcError as e:
          num_retries += 1
          if num_retries > max_retries:
            raise e
          time.sleep(1)

    options_response = send_options_request()

    def add_runner_options(parser):
      for option in options_response.options:
        try:
          # no default values - we don't want runner options
          # added unless they were specified by the user
          add_arg_args = {'action': 'store', 'help': option.description}
          if option.type == beam_job_api_pb2.PipelineOptionType.BOOLEAN:
            add_arg_args['action'] = 'store_true' \
              if option.default_value != 'true' else 'store_false'
          elif option.type == beam_job_api_pb2.PipelineOptionType.INTEGER:
            add_arg_args['type'] = int
          elif option.type == beam_job_api_pb2.PipelineOptionType.ARRAY:
            add_arg_args['action'] = 'append'
          parser.add_argument("--%s" % option.name, **add_arg_args)
        except Exception as e:
          # ignore runner options that are already present
          # only in this case is duplicate not treated as error
          if 'conflicting option string' not in str(e):
            raise
          _LOGGER.debug("Runner option '%s' was already added" % option.name)

    all_options = self.options.get_all_options(
        add_extra_args_fn=add_runner_options,
        retain_unknown_options=self._retain_unknown_options)

    return self.encode_pipeline_options(all_options)

  @staticmethod
  def encode_pipeline_options(
      all_options: Dict[str, Any]) -> 'struct_pb2.Struct':
    def convert_pipeline_option_value(v):
      # convert int values: BEAM-5509
      if type(v) == int:
        return str(v)
      elif isinstance(v, ValueProvider):
        return convert_pipeline_option_value(
            v.get()) if v.is_accessible() else None
      return v

    # TODO: Define URNs for options.
    p_options = {
        'beam:option:' + k + ':v1': convert_pipeline_option_value(v)
        for k, v in all_options.items() if v is not None
    }
    return job_utils.dict_to_struct(p_options)

  def prepare(
      self, proto_pipeline: beam_runner_api_pb2.Pipeline
  ) -> beam_job_api_pb2.PrepareJobResponse:
    """Prepare the job on the job service"""
    return self.job_service.Prepare(
        beam_job_api_pb2.PrepareJobRequest(
            job_name='job',
            pipeline=proto_pipeline,
            pipeline_options=self.get_pipeline_options()),
        timeout=self.timeout)

  def stage(
      self,
      proto_pipeline: beam_runner_api_pb2.Pipeline,
      artifact_staging_endpoint,
      staging_session_token) -> None:
    """Stage artifacts"""
    if artifact_staging_endpoint:
      artifact_service.offer_artifacts(
          beam_artifact_api_pb2_grpc.ArtifactStagingServiceStub(
              channel=grpc.insecure_channel(artifact_staging_endpoint)),
          artifact_service.ArtifactRetrievalService(
              artifact_service.BeamFilesystemHandler(None).file_reader),
          staging_session_token)

  def run(
      self, preparation_id: str
  ) -> Tuple[str,
             Iterator[beam_job_api_pb2.JobStateEvent],
             Iterator[beam_job_api_pb2.JobMessagesResponse]]:
    """Run the job"""
    try:
      state_stream = self.job_service.GetStateStream(
          beam_job_api_pb2.GetJobStateRequest(job_id=preparation_id),
          timeout=self.timeout)
      # If there's an error, we don't always get it until we try to read.
      # Fortunately, there's always an immediate current state published.
      state_stream = itertools.chain([next(state_stream)], state_stream)
      message_stream = self.job_service.GetMessageStream(
          beam_job_api_pb2.JobMessagesRequest(job_id=preparation_id),
          timeout=self.timeout)
    except Exception:
      # TODO(https://github.com/apache/beam/issues/19284): Unify preparation_id
      # and job_id for all runners.
      state_stream = message_stream = None

    # Run the job and wait for a result, we don't set a timeout here because
    # it may take a long time for a job to complete and streaming
    # jobs currently never return a response.
    run_response = self.job_service.Run(
        beam_job_api_pb2.RunJobRequest(preparation_id=preparation_id))

    if state_stream is None:
      state_stream = self.job_service.GetStateStream(
          beam_job_api_pb2.GetJobStateRequest(job_id=run_response.job_id))
      message_stream = self.job_service.GetMessageStream(
          beam_job_api_pb2.JobMessagesRequest(job_id=run_response.job_id))

    return run_response.job_id, message_stream, state_stream


class PortableRunner(runner.PipelineRunner):
  """
    Experimental: No backward compatibility guaranteed.
    A BeamRunner that executes Python pipelines via the Beam Job API.

    This runner is a stub and does not run the actual job.
    This runner schedules the job on a job service. The responsibility of
    running and managing the job lies with the job service used.
  """
  def __init__(self):
    self._dockerized_job_server: Optional[job_server.JobServer] = None

  @staticmethod
  def _create_environment(options: PipelineOptions) -> environments.Environment:
    return environments.Environment.from_options(
        options.view_as(PortableOptions))

  def default_job_server(self, options):
    raise NotImplementedError(
        'You must specify a --job_endpoint when using --runner=PortableRunner. '
        'Alternatively, you may specify which portable runner you intend to '
        'use, such as --runner=FlinkRunner or --runner=SparkRunner.')

  def create_job_service_handle(self, job_service, options) -> JobServiceHandle:
    return JobServiceHandle(job_service, options)

  def create_job_service(self, options: PipelineOptions) -> JobServiceHandle:
    """
    Start the job service and return a `JobServiceHandle`
    """
    job_endpoint = options.view_as(PortableOptions).job_endpoint
    if job_endpoint:
      if job_endpoint == 'embed':
        server: job_server.JobServer = job_server.EmbeddedJobServer()
      else:
        job_server_timeout = options.view_as(PortableOptions).job_server_timeout
        server = job_server.ExternalJobServer(job_endpoint, job_server_timeout)
    else:
      server = self.default_job_server(options)
    return self.create_job_service_handle(server.start(), options)

  @staticmethod
  def get_proto_pipeline(
      pipeline: Pipeline,
      options: PipelineOptions) -> beam_runner_api_pb2.Pipeline:
    proto_pipeline = pipeline.to_runner_api(
        default_environment=environments.Environment.from_options(
            options.view_as(PortableOptions)))

    return PortableRunner._optimize_pipeline(proto_pipeline, options)

  @staticmethod
  def _optimize_pipeline(
      proto_pipeline: beam_runner_api_pb2.Pipeline,
      options: PipelineOptions) -> beam_runner_api_pb2.Pipeline:
    # TODO: https://github.com/apache/beam/issues/19493
    # Eventually remove the 'pre_optimize' option alltogether and only perform
    # the equivalent of the 'default' case below (minus the 'lift_combiners'
    # part).
    pre_optimize = options.view_as(DebugOptions).lookup_experiment(
        'pre_optimize', 'default').lower()
    if (not options.view_as(StandardOptions).streaming and
        pre_optimize != 'none'):
      if pre_optimize == 'default':
        phases = [
            # TODO: https://github.com/apache/beam/issues/18584
            #       https://github.com/apache/beam/issues/18586
            # Eventually remove the 'lift_combiners' phase from 'default'.
            translations.pack_combiners,
            translations.lift_combiners,
            translations.sort_stages
        ]
        partial = True
      elif pre_optimize == 'all':
        phases = translations.standard_optimize_phases()
        partial = False
      elif pre_optimize == 'all_except_fusion':
        # TODO(https://github.com/apache/beam/issues/19422): Delete this branch
        # after PortableRunner supports beam:runner:executable_stage:v1.
        phases = translations.standard_optimize_phases()
        phases.remove(translations.greedily_fuse)
        partial = True
      else:
        phases = []
        for phase_name in pre_optimize.split(','):
          # For now, these are all we allow.
          if phase_name in ('pack_combiners', 'lift_combiners'):
            phases.append(getattr(translations, phase_name))
          else:
            raise ValueError(
                'Unknown or inapplicable phase for pre_optimize: %s' %
                phase_name)
        phases.append(translations.sort_stages)
        partial = True

      # All (known) portable runners (ie Flink and Spark) support these URNs.
      known_urns = frozenset([
          common_urns.composites.RESHUFFLE.urn,
          common_urns.primitives.IMPULSE.urn,
          common_urns.primitives.FLATTEN.urn,
          common_urns.primitives.GROUP_BY_KEY.urn
      ])
      proto_pipeline = translations.optimize_pipeline(
          proto_pipeline,
          phases=phases,
          known_runner_urns=known_urns,
          partial=partial)

    return proto_pipeline

  def run_portable_pipeline(
      self, pipeline: beam_runner_api_pb2.Pipeline,
      options: PipelineOptions) -> runner.PipelineResult:
    portable_options = options.view_as(PortableOptions)

    # Do not set a Runner. Otherwise this can cause problems in Java's
    # PipelineOptions, i.e. ClassNotFoundException, if the corresponding Runner
    # does not exist in the Java SDK. In portability, the entry point is clearly
    # defined via the JobService.
    portable_options.view_as(StandardOptions).runner = None

    cleanup_callbacks = self.start_and_replace_loopback_environments(
        pipeline, options)

    optimized_pipeline = self._optimize_pipeline(pipeline, options)
    job_service_handle = self.create_job_service(options)
    job_id, message_stream, state_stream = job_service_handle.submit(
        optimized_pipeline)

    result = PipelineResult(
        job_service_handle.job_service,
        job_id,
        message_stream,
        state_stream,
        cleanup_callbacks)
    if cleanup_callbacks:
      # Register an exit handler to ensure cleanup on exit.
      atexit.register(functools.partial(result._cleanup, on_exit=True))
      _LOGGER.info(
          'Environment "%s" has started a component necessary for the '
          'execution. Be sure to run the pipeline using\n'
          '  with Pipeline() as p:\n'
          '    p.apply(..)\n'
          'This ensures that the pipeline finishes before this program exits.',
          portable_options.environment_type)
    return result

  @staticmethod
  def start_and_replace_loopback_environments(pipeline, options):
    portable_options = copy.deepcopy(options.view_as(PortableOptions))
    experiments = options.view_as(DebugOptions).experiments or []
    cleanup_callbacks = []
    for env in pipeline.components.environments.values():
      if env.urn == python_urns.EMBEDDED_PYTHON_LOOPBACK:
        # Start a worker and change the environment to point to that worker.
        use_loopback_process_worker = options.view_as(
            DebugOptions).lookup_experiment(
                'use_loopback_process_worker', False)
        portable_options.environment_type = 'EXTERNAL'
        portable_options.environment_config, server = (
            worker_pool_main.BeamFnExternalWorkerPoolServicer.start(
                state_cache_size=
                sdk_worker_main._get_state_cache_size_bytes(
                  options=options),
                data_buffer_time_limit_ms=
                sdk_worker_main._get_data_buffer_time_limit_ms(experiments),
                use_process=use_loopback_process_worker))
        external_env = environments.ExternalEnvironment.from_options(
            portable_options).to_runner_api(None)  # type: ignore
        env.urn = external_env.urn
        env.payload = external_env.payload
        cleanup_callbacks.append(functools.partial(server.stop, 1))
    return cleanup_callbacks


class PortableMetrics(metric.MetricResults):
  def __init__(self, job_metrics_response):
    metrics = job_metrics_response.metrics
    self.attempted = portable_metrics.from_monitoring_infos(metrics.attempted)
    self.committed = portable_metrics.from_monitoring_infos(metrics.committed)

  @staticmethod
  def _combine(committed, attempted, filter):
    all_keys = set(committed.keys()) | set(attempted.keys())
    return [
        MetricResult(key, committed.get(key), attempted.get(key))
        for key in all_keys if metric.MetricResults.matches(filter, key)
    ]

  def query(self, filter=None):
    counters, distributions, gauges, stringsets, bounded_tries = [
        self._combine(x, y, filter)
        for x, y in zip(self.committed, self.attempted)
    ]

    return {
        self.COUNTERS: counters,
        self.DISTRIBUTIONS: distributions,
        self.GAUGES: gauges,
        self.STRINGSETS: stringsets,
        self.BOUNDED_TRIES: bounded_tries,
    }


class PipelineResult(runner.PipelineResult):
  def __init__(
      self,
      job_service,
      job_id,
      message_stream,
      state_stream,
      cleanup_callbacks=()):
    super().__init__(beam_job_api_pb2.JobState.UNSPECIFIED)
    self._job_service = job_service
    self._job_id = job_id
    self._messages = []
    self._message_stream = message_stream
    self._state_stream = state_stream
    self._cleanup_callbacks = cleanup_callbacks
    self._metrics = None
    self._runtime_exception = None

  def cancel(self) -> None:
    try:
      self._job_service.Cancel(
          beam_job_api_pb2.CancelJobRequest(job_id=self._job_id))
    finally:
      self._cleanup()

  @property
  def state(self):
    runner_api_state = self._job_service.GetState(
        beam_job_api_pb2.GetJobStateRequest(job_id=self._job_id)).state
    self._state = self.runner_api_state_to_pipeline_state(runner_api_state)
    return self._state

  @staticmethod
  def runner_api_state_to_pipeline_state(runner_api_state):
    return getattr(
        runner.PipelineState,
        beam_job_api_pb2.JobState.Enum.Name(runner_api_state))

  @staticmethod
  def pipeline_state_to_runner_api_state(pipeline_state):
    if pipeline_state == runner.PipelineState.PENDING:
      return beam_job_api_pb2.JobState.STARTING
    else:
      try:
        return beam_job_api_pb2.JobState.Enum.Value(pipeline_state)
      except ValueError:
        return beam_job_api_pb2.JobState.UNSPECIFIED

  def metrics(self):
    if not self._metrics:

      job_metrics_response = self._job_service.GetJobMetrics(
          beam_job_api_pb2.GetJobMetricsRequest(job_id=self._job_id))

      self._metrics = PortableMetrics(job_metrics_response)
    return self._metrics

  def _last_error_message(self) -> str:
    # Filter only messages with the "message_response" and error messages.
    messages = [
        m.message_response for m in self._messages
        if m.HasField('message_response')
    ]
    error_messages = [
        m for m in messages
        if m.importance == beam_job_api_pb2.JobMessage.JOB_MESSAGE_ERROR
    ]
    if error_messages:
      return error_messages[-1].message_text
    else:
      return 'unknown error'

  def wait_until_finish(self, duration=None):
    """
    :param duration: The maximum time in milliseconds to wait for the result of
    the execution. If None or zero, will wait until the pipeline finishes.
    :return: The result of the pipeline, i.e. PipelineResult.
    """
    last_error_text = None

    def read_messages() -> None:
      nonlocal last_error_text
      previous_state = -1
      for message in self._message_stream:
        if message.HasField('message_response'):
          mr = message.message_response
          logging.log(MESSAGE_LOG_LEVELS[mr.importance], "%s", mr.message_text)
          if mr.importance == beam_job_api_pb2.JobMessage.JOB_MESSAGE_ERROR:
            last_error_text = mr.message_text
        else:
          current_state = message.state_response.state
          if current_state != previous_state:
            _LOGGER.info(
                "Job state changed to %s",
                self.runner_api_state_to_pipeline_state(current_state))
            previous_state = current_state
        self._messages.append(message)

    message_thread = threading.Thread(
        target=read_messages, name='wait_until_finish_read')
    message_thread.daemon = True
    message_thread.start()

    if duration:
      state_thread = threading.Thread(
          target=functools.partial(self._observe_state, message_thread),
          name='wait_until_finish_state_observer')
      state_thread.daemon = True
      state_thread.start()
      start_time = time.time()
      duration_secs = duration / 1000
      while (time.time() - start_time < duration_secs and
             state_thread.is_alive()):
        time.sleep(1)
    else:
      self._observe_state(message_thread)

    if self._runtime_exception:
      raise self._runtime_exception
    from apache_beam.runners.runner import PipelineState
    if self._state == PipelineState.FAILED:
      raise RuntimeError(last_error_text or "Pipeline failed.")

    return self._state

  def _observe_state(self, message_thread):
    try:
      for state_response in self._state_stream:
        self._state = self.runner_api_state_to_pipeline_state(
            state_response.state)
        if state_response.state in TERMINAL_STATES:
          # Wait for any last messages.
          message_thread.join(10)
          break
      if self._state != runner.PipelineState.DONE:
        self._runtime_exception = RuntimeError(
            'Pipeline %s failed in state %s: %s' %
            (self._job_id, self._state, self._last_error_message()))
    except Exception as e:
      self._runtime_exception = e
    finally:
      self._cleanup()

  def _cleanup(self, on_exit: bool = False) -> None:
    if on_exit and self._cleanup_callbacks:
      _LOGGER.info(
          'Running cleanup on exit. If your pipeline should continue running, '
          'be sure to use the following syntax:\n'
          '  with Pipeline() as p:\n'
          '    p.apply(..)\n'
          'This ensures that the pipeline finishes before this program exits.')
    callback_exceptions = []
    for callback in self._cleanup_callbacks:
      try:
        callback()
      except Exception as e:
        callback_exceptions.append(e)

    self._cleanup_callbacks = ()
    if callback_exceptions:
      formatted_exceptions = ''.join(
          [f"\n\t{repr(e)}" for e in callback_exceptions])
      raise RuntimeError('Errors: {}'.format(formatted_exceptions))
